Counting dark matter particles in LHC events

TL;DR

This paper proposes a novel method to count invisible particles in LHC events by analyzing distribution shapes, aiming to identify stable Dark Matter candidates and gain insights into their properties.

Contribution

It introduces a new technique for counting invisible particles in collider data, which can distinguish Dark Matter particles from neutrinos and other invisible entities.

Findings

Successfully simulated counting neutrinos in Standard Model events

Demonstrated potential to identify Dark Matter candidates in new physics scenarios

Analyzed effects influencing measurement accuracy

Abstract

We suggest trying to count the number of invisible particles produced in missing energy events at the LHC, arguing that multiple production of such particles provides evidence that they constitute stable Dark Matter and that counting them could yield further insights into the nature of Dark Matter. We propose a method to count invisible particles, based on fitting the shapes of certain transverse- or invariant-mass distributions, discuss various effects that may affect the measurement, and simulate the use of the method to count neutrinos in Standard Model processes and Dark Matter candidates in new physics processes.